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Related Experiment Video

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Brain-model neural similarity reveals abstractive summarization performance.

Zhejun Zhang1, Shaoting Guo1, Wenqing Zhou1

  • 1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.

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Deep language models (DLMs) show increasing brain-like patterns with layer depth. Deeper layers are crucial for summarization performance, mirroring human language processing.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Neuroscience

Background:

  • Deep language models (DLMs) demonstrate advanced language abilities, prompting comparisons with human cognitive processes.
  • Understanding the neural basis of language in DLMs is key to improving their performance.

Purpose of the Study:

  • To investigate representational similarity (RS) between DLMs and the human brain for abstractive summarization (ABS).
  • To correlate this similarity with ABS task performance.

Main Methods:

  • Representational Similarity Analysis (RSA) to compare hidden layer patterns of BART, PEGASUS, and T5 models with human brain language RPs.
  • Layer-wise ablation (attention, noise) to assess the impact of hidden layers on model performance.

Main Results:

  • Increasing layer depth in DLMs correlates with higher representational similarity to human brain language processing.
  • Ablation of deeper layers significantly degrades summarization performance more than shallower layers.
  • Higher similarity between DLM hidden layers and human brain activity is significantly linked to better model performance.

Conclusions:

  • Deeper layers in DLMs are critical for integrating information and achieving higher summarization performance.
  • DLM representations align with human brain language processing, offering insights for model optimization.
  • Findings suggest aligning DLMs with human neural mechanisms can enhance language understanding and generation.